Bibliographic record
Abstract
Basic circuit analysis is a core course in most of the undergraduate engineering programs and is the prerequisite course for many other courses in the undergraduate electrical engineering program. Students enter into engineering schools with varying knowledge of the concepts of basic circuit analysis depending on whether they come from high school, CEGEP, or a technical college, etc. Many students from all engineering majors struggle to learn the concepts taught in these courses which creates challenges for both faculty members and students in courses when for which basic circuit analysis is a pre-requisite course. There is more research done in understanding the conceptual knowledge of physics of electricity and electric (and electronic) components and improving the instruction of basic circuit analysis concepts, but not enough work is done to understand the mistakes undergraduate electrical engineering students continue to make course after course. For this study, the authors look at the persistent problems in learning circuit analysis techniques by looking at students’ use of these techniques in three core courses in electrical engineering program namely electronics 1, electronics 2 and electromagnetic waves and guiding structures. Students’ responses to exam questions that specifically expected students to use these concepts are analyzed. The objective of the study was to analyze whether the understanding of the application of circuit analysis techniques get better as students continue to use these concepts in more courses and applications, or the problems persist. Results show that the students persistently make mistakes in applying KVL and KCL equations, nodal analysis, superposition theorem, voltage divider, and mesh analysis. Additionally, the results reveal that students persistently make mistakes in questions that involve the concepts of load and no load, open circuit, series components, parallel components, voltage drop across the current source, and voltage gain. It is noted that the mistakes made by students do not get much better as they continue taking more courses. The results of this study are important from many aspects. They are helpful to understand the continuing struggles of students and so are helpful to design pedagogy and assessment in a way that these concepts can be well explained. Thorough understanding of the concepts in a course that is as important as basic circuit analysis is important to achieve many engineering education goals including student retention, motivation, innovation, and inclusion.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".